Background: This study examined the social well-being of single older adults through the companionship of a social robot, LOVOT (Love+Robot; Groove X). It is designed as a companion for older adults, providing love and affection through verbal and physical interaction. We investigated older adults’ perceptions of the technology and how they benefitted from interacting with LOVOT, to guide the future development of social robots. Objective: This study aimed to use a phenomenological research design to understand the participants’ experiences of companionship provided by the social robot. Our research focused on (1) examining the social well-being of single older adults through the companionship of social robots and (2) understanding the perceptions of single older adults when interacting with social robots. Given the prevalence of technology use to support aging, understanding single older adults’ social well-being and their perceptions of social robots is essential to guide future research on and design of social robots. Methods: A total of 5 single women, aged 60 to 75 years, participated in the study. The participants interacted independently with the robot for a week in their own homes and then participated in a poststudy interview to share their experiences. Results: In total, 4 main themes emerged from the participants’ interactions with LOVOT, such as caring for a social robot, comforting presence of the social robot, meaningful connections with the social robot, and preference for LOVOT over pets. Conclusions: The results indicate that single older adults can obtain psychosocial support by interacting with LOVOT. LOVOT is easily accepted as a companion and makes single older adults feel like they have a greater sense of purpose and someone to connect with. This study suggests that social robots can provide companionship to older adults who live alone. Social robots can help alleviate loneliness by allowing single older adults to form social connections with robots as companions. These findings are particularly important given the rapid aging of the population and the increasing number of single-person households in Singapore.

Purpose: The world population is ageing, along with an increasing possibility of functional limitations that affect independent living. Assistive technologies such as exoskeletons for rehabilitative purposes and daily activities assistance maintaining the independence of people with disabilities, especially older adults who wish to ageing-in-place. The purpose of this systematic integrative review was threefold: to explore the development team compositions and involvement, to understand the recruitment and engagement of stakeholders, and to synthesise reported or anticipated consequences of multidisciplinary collaboration. Methods: Databases searched included PubMed, CINAHL Plus, PsycINFO, Web of Science, Scopus, and IEEE Xplore. A total of 34 studies that reported the development of exoskeleton adopting user-centered design (UCD) method in healthcare or community settings that were published in English from 2000 to July 2022 were included. Results: Three major trends emerged from the analysis of included studies. First, there is a need to redefine multidisciplinary collaboration, from within-discipline collaboration to cross-discipline collaboration. Second, the level of engagement of stakeholders during the exoskeleton development remained low. Third, there was no standardised measurement to quantify knowledge production currently. Conclusion: As suggested by the synthesised results in this review, exoskeleton development has been increasing to improve the functioning of people with disabilities. Exoskeleton development often required expertise from different disciplines and the involvement of stakeholders to increase acceptance, thus we propose the Multidisciplinary Collaboration Appraisal Tool to assess multidisciplinary collaboration using the UCD approach. Future research is required to understand the effectiveness of multidisciplinary collaboration on exoskeleton development using the UCD approach.

Background: Given the rapidly aging nature of our global population, policy makers around the world are now emphatically promoting active aging. To address the psychosocial needs of older persons and support active aging, researchers are exploring the use of assistive technologies, specifically social robots as companions. However, there is limited evidence on the efficacy of social robots in promoting active aging for older people in the Hong Kong and Singapore contexts. Objective: This study presents the protocol of a study that investigates the acceptance and quality of interaction between a Japanese social robot, LOVOT, and single older adults in Hong Kong and Singapore. Methods: We used a baseline assessment to measure the primary outcome, participants’ acceptance of technology, and a sense of loneliness, namely, the participants’ differences in responses to LOVOT before and following their interaction with the social robot in this multimethod study design. The baseline assessment consisted of the Qualtrics survey, which measures senior technology acceptance, loneliness, older people’s quality of life, subjective happiness, cultural values, willingness to pay, and demographic characteristics, along with the LOVOT’s sociability and system usability. In the study, participants interacted with LOVOT in 3 sessions before being surveyed to measure the older people’s acceptance and attitudes toward LOVOT. A pre–social robot intervention also occurred in the first session. The study was conducted in both Hong Kong and Singapore. A total of 15 single older adults (ie, individuals who live alone) from Hong Kong and another 15 from Singapore were recruited. Participants were 60-75 years of age, lived by themselves, and had no known cognitive or mental issues. Results: The study began recruiting in March 2022, and recruitment was completed at the end of October 2022. Data collection and data set construction were completed at the end of January 2023. Analysis of the data is currently being conducted, and we plan to publish the results by mid-2023. Conclusions: At an individual level, the study will clarify if LOVOT influences single older adults’ psychosocial well-being by reducing their loneliness. At a community level, the study’s findings will illustrate whether LOVOT can provide increased social connectedness while decreasing individual loneliness. Last, this study’s conclusions can inform policy makers to provide social robots to older people to improve their quality of life. Findings can also inform gerontechnology developers on which aspects and cultural considerations to take into account for future inventions. International Registered Report Identifier (IRRID): DERR1-10.2196/48618

Spiritual well-being (SWB) is a key construct in a holistic health model. This study applied a theory-driven mobile health-supported volunteer-assisted self-help (mVS) intervention to enhance SWB for discharged older patients through a medical-social-academia tripartite collaboration. This study followed a quasi-experimental design, conducted from 2017 to 2019. Data were collected from pre- and post-intervention assessment and Clinical Management System under the Hospital Authority in Hong Kong. SWB was assessed by the Spirituality Scale for Chinese Elders with seven domains, a validated scale suitable for the non-religious context. A total of 161 discharged older patients were recruited, 101 received the mVS intervention and 60 received standardized usual volunteer visits. mVS intervention was assisted by volunteers supported by a corresponding mobile application Fu Le Man Xin. The final analysis included 137 older patients living in Hong Kong aged 61 to 94 years. Repeated measures ANOVA showed mVS intervention significantly enhanced two domains of SSCE: meaning of life (F(1, 64) = 4.029, p = .049) and relationship with others (F(1, 57) = 6.428, p = .014). This study shows that mVS intervention is a feasible medical-social-academia tripartite collaboration that improves two domains of the SWB of discharged older patients: meaning of life and relationship with others.

In this ageing society, sarcopenia as a geriatric condition that can have significant negative impacts on an individual’s quality of life. Sarcopenia is a kind of aged syndrome associated with loss of muscle mass and function, which may lead to falls, fractures, gait disorders or even mortality. There are multiple ways to diagnose sarcopenia, such as using Magnetic resonance imaging (MRI), Dual-energy X-ray absorptiometry (DEXA) and Bioelectrical impedance analysis (BIA) etc. to calculate muscle mass; using handgrip or sit-to-stand to measure muscle strength; using short physical performance battery (SPPB), gait, and 5-time sit-to-stand to evaluate physical performance. In this work, we use two μIMUs worn on subjects to record their sit-to-stand motion, and then used several machine learning models to diagnose the severity of sarcopenia of the subjects. We recruited 53 elderly subjects in total for this work. The youngest subject is 65 years old and the oldest is 84 years old. Their average age is 70 years old. Among these 53 subjects, there are 12 healthy ones and 41 sarcopenia patients with different severity. The subject is instructed to do the single sit-to-stand (STS) three times, and two μIMUs attached to the subject’s waist and thigh transfer the data to a computer by Bluetooth. We separated the STS motion process into 4 phases based on the angle and angular velocity, extracted a total of 510 features for motion analytics. These features were futher analyzed by sequential feature selection with 5 different machine learning models (SVM, KNN, decision tree, LDA, and multilayer perceptron). With our proposed methodology, all 53 subjects could be classified as healthy or having sarcopenia with risk level 1, 2, or 3. The best accuracy to distinguish the healthy or sarcopenia subjects is 98.32%, and the best results to distinguish sarcopenia risk levels from 0 (healthy) to 3 (most severe) is 90.44%.